An order-aware dataflow model for parallel Unix pipelines

We present a dataflow model for modelling parallel Unix shell pipelines. To accurately capture the semantics of complex Unix pipelines, the dataflow model is order-aware, i.e., the order in which a node in the dataflow graph consumes inputs from different edges plays a central role in the semantics of the computation and therefore in the resulting parallelization. We use this model to capture the semantics of transformations that exploit data parallelism available in Unix shell computations and prove their correctness. We additionally formalize the translations from the Unix shell to the dataflow model and from the dataflow model back to a parallel shell script. We implement our model and transformations as the compiler and optimization passes of a system parallelizing shell pipelines, and use it to evaluate the speedup achieved on 47 pipelines.

StreamGen

Distributed streaming applications, i.e., applications that process massive streams of data in a distributed fashion, are becoming increasingly popular to tame the velocity and the volume of Big Data . Nevertheless, the widespread adoption of data-intensive processing is still limited by the non-trivial design paradigms involved, which deal with the unboundedness and volume of involved data streams and by the many distributed streaming platforms, each with its own characteristics and APIs. In this article, we present StreamGen, a Model-Driven Engineering tool to simplify the design of such streaming applications and automatically generate the corresponding code. StreamGen is able to automatically generate fully working and processing-ready code for different target platforms (e.g., Apache Spark, Apache Flink). Evaluation shows that (i) StreamGen is general enough to model and generate the code, offering comparable performance against a preexisting similar and well-known application; (ii) the tool is fully compliant with streaming concepts defined as part of the Google Dataflow Model; and (iii) users with little computer science background and limited experience with big data have been able to work with StreamGen and create/refactor an application in a matter of minutes.

Software synthesis from dataflow schedule graphs

The dataflow-model of computation is widely used in design and implementation of signal processing systems. In dataflow-based design processes, scheduling—the assignment and coordination of computational modules across processing resources—is a critical task that affects practical measures of performance, including latency, throughput, energy consumption, and memory requirements. Dataflow schedule graphs (DSGs) provide a formal abstraction for representing schedules in dataflow-based design processes. The DSG abstraction allows designers to model a schedule as a separate dataflow graph, thereby providing a formal, abstract (platform- and language-independent) representation for the schedule. In this paper, we introduce a design methodology that is based on explicit specifications of application graphs and schedules as cooperating dataflow models. We also develop new techniques and tools for automatically synthesizing efficient implementations on multicore platforms from these coupled application and schedule models. We demonstrate the proposed methodology and synthesis techniques through a case study involving real-time detection of people and vehicles using acoustic and seismic sensors.

Dataflow analysis of vehicle safety computers regarding high-speed maglev transportation based on data priorities

To analyze the dataflow of vehicle safety computers regarding high-speed maglev transportation, it is considerable to add data priorities to the dataflow model of vehicle safety computers to improve the accuracy. With regard to vehicle safety computers, we choose VSC1 as our research object. First, we give a brief summary for the interface relationships of VSC1. Next, we analyze the data priorities of VSC1 in detail. After that, we present the dataflow model of VSC1 with priorities. Finally, we make a brief conclusion. The structure of the abstract of an article is strictly arranged and should encompass the following points: Background: Dataflow model of Vehicle Safety Computers regarding high-speed maglev transportation. Aim: To analyze the dataflow of vehicle safety computers with data priorities. Methods: interface analysis, data priority analysis and dataflow chart. Results: The dataflow model of VSC1 with data priorities is presented. Conclusion: Adding data priorities to the dataflow model of vehicle safety computers to improve the accuracy is fulfilled. The recommended number of words in the abstract is 500.

Automatic water level control using LabVIEW

Shortage in water supply is one of the major issues that some major cities throughout the world are facing nowadays. Due to not having full day water supply, households will have to efficiently manage the problem of water shortage and overcome the crises. This paper presents a system that indicates and controls the level of water in overhead tanks. Ultra-sonic sensors are employed to detect the level of the water between predefined minimum and maximum levels. LabVIEW which is a graphical programming language that uses a dataflow model is used to program microcontroller board Arduino UNO that is an interface between the software and the rest of the circuit components. From measured results good performance and accurate results are achieved.